Characterized in part by the deep, many-layered neural networks, a
prevailing idea of the deep learning paradigm is that machine learning
systems can become more accurate and flexible when we allow for abstract
representations of data to be successively learned, rather than handcrafted
through classical feature engineering. For a recent general survey
on deep learning confer Schmidhuber [1]}, and
for a more explicit discussion of deep learning in natural language
processing Socher & Manning [2]}.
